LOWLAD: A locally weighted \(L_ 1\) smoothing spline algorithm with cross validated choice of smoothing parameters

DOI10.1007/BF02143928zbMath0828.65010OpenAlexW2055948493WikidataQ58092099 ScholiaQ58092099MaRDI QIDQ1893530

Upmanu Lall, Ken W. Bosworth

Publication date: 10 January 1996

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02143928

zbMATH Keywords

robust regressioncross validationchoice of smoothing parametersleast absolute deviations smoothing splinesmoothing and regression splines

Mathematics Subject Classification ID

Numerical computation using splines (65D07) Numerical smoothing, curve fitting (65D10) Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (1)

LOWLAD

Uses Software

• FSQP
• LINPACK
• GAMS
• NPSOL

Cites Work

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• Linear smoothers and additive models
• A comparison of Kriging with nonparametric regression methods
• An efficient method for calculating smoothing splines using orthogonal transformations
• Variable bandwidth kernel estimators of regression curves
• Multivariate adaptive regression splines
• An \(L_ 1\) smoothing spline algorithm with cross validation
• Decomposition and reconstruction algorithms for spline wavelets on a bounded interval
• A general method for the construction of interpolating or smoothing spline-functions
• Kriging Nonstationary Data
• Weighted Local Regression and Kernel Methods for Nonparametric Curve Fitting
• A Unifying Approach to Nonparametric Regression Estimation
• The ∏ Method for Estimating Multivariate Functions from Noisy Data
• Multivariate Smoothing and Interpolating Splines
• Convex Analysis
• Smoothing noisy data with spline functions

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